Unverified Commit 141e77c9 authored by Mayank Mittal's avatar Mayank Mittal Committed by GitHub

Adds dark theme images for multi-GPU and actuators (#601)

# Description

Previously, the images were rendered as "white" even in the dark theme
of the documentation. This MR fixes the images and adds their
counterpart for dark theme.

## Type of change

- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have run all the tests with `./isaaclab.sh --test` and they pass
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there
parent d06b1f91
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...@@ -59,10 +59,17 @@ actuator model, such as a DC motor, would require configuring a different actuat ...@@ -59,10 +59,17 @@ actuator model, such as a DC motor, would require configuring a different actuat
The following figure shows the actuator groups for a legged mobile manipulator: The following figure shows the actuator groups for a legged mobile manipulator:
.. image:: ../_static/actuator_groups.svg .. image:: ../_static/actuator-group/actuator-light.svg
:width: 600 :class: only-light
:align: center :align: center
:alt: Actuator groups for a legged mobile manipulator :alt: Actuator models for a legged mobile manipulator
:width: 80%
.. image:: ../_static/actuator-group/actuator-dark.svg
:class: only-dark
:align: center
:width: 80%
:alt: Actuator models for a legged mobile manipulator
.. seealso:: .. seealso::
......
...@@ -27,9 +27,17 @@ Each process collects its own rollouts during the training process and has its o ...@@ -27,9 +27,17 @@ Each process collects its own rollouts during the training process and has its o
network. During training, gradients are aggregated across the processes and broadcasted back to the process network. During training, gradients are aggregated across the processes and broadcasted back to the process
at the end of the epoch. at the end of the epoch.
.. image:: ../_static/multigpu.png .. image:: ../_static/multi-gpu-rl/a3c-light.svg
:class: only-light
:align: center :align: center
:alt: Multi-GPU training paradigm :alt: Multi-GPU training paradigm
:width: 80%
.. image:: ../_static/multi-gpu-rl/a3c-dark.svg
:class: only-dark
:align: center
:width: 80%
:alt: Multi-GPU training paradigm
To train with multiple GPUs, use the following command, where ``--proc_per_node`` represents the number of available GPUs: To train with multiple GPUs, use the following command, where ``--proc_per_node`` represents the number of available GPUs:
......
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